1. Field of the Invention
The present invention relates, in general, to systems and methods of refining dynamic fitness functions of genetic algorithms. More specifically, the present invention includes systems and methods of refining search-based fitness functions based on user-selected search results.
2. Relevant Background
Conventional Internet search engines use heuristics to determine which web pages are the best match for a keyword search. This process includes measuring the relevancy of a web page based on the number of times the page is cited by (or hyperlinked to) other web pages.
This method of measuring relevancy is inherently bias towards selecting older web pages that have had more time to develop cites and links to other web pages. Unfortunately, the information found on these older web pages is more likely to have outdated information than more recently created web pages. This bias can actually slow the dissemination of new information displayed on new web pages.
Another problem with conventional search engines is their inability to use semantics and contextual associations while searching keywords. For example, when a user inputs the keyword “title” into a conventional search engine, the engine cannot differentiate whether “title” refers to the title of a book or whether “title” refers to a house or car title. Thus, there remains a need for search engines that are more discriminating about semantic differences in words like humans.
One approach to these and other problems with conventional search engines is to use genetic algorithms (also called genetic programs) that can provide globally optimum solutions in problem spaces that cannot be well defined mathematically. These problem spaces generally involve complex tradeoffs between competing considerations, and the genetic algorithm attempts to determine the best balance between competing considerations.
A problem with genetic algorithms, however, is that they are difficult to implement when the relative quality of the results are dynamic or difficult to completely define. For example, the quality of a keyword search depends on the semantics of the keywords and their contextual association with each other. These semantics are dynamic and context dependent, making them difficult to implement in a genetic algorithm.
A similar problem occurs in searches of rich media depositories that contain substantial amounts of graphical and/or pictorial information. The textual metadata associated with this kind of data generally provides an incomplete description, if such metadata descriptions are present at all. Like keywords, the metadata depends on semantics and is difficult to search effectively with genetic algorithms. Thus, there remains a need for systems and methods that employ genetic algorithms that work effectively with dynamic and difficult to define semantics of human language.